The subject disclosure is directed to the transportation arts, trip planning arts, the data processing arts, the data analysis arts, the tracking arts, the predictive arts, and the like.
Many cities and agglomerations have produced a variety of journey planners in the form of a web or mobile application. Upon a user travel request, these planners are configured to generate journeys, i.e., trips on a transportation system via public or private transportation means, using a static library of roads and public transportation network and services attributes and data.
A public transport trip planner, or journey planner, may be designed to provide information about available public transport journeys or routes along the public transportation system, for example via a Web-based application. Such an application may prompt a prospective traveler to input an origin and a destination, and then use a trip planning engine to determine a route between the two input locations using specified available public transportation services and routes, e.g., buses, trams, trains, etc., depending on available schedules for these services. Accordingly, transportation authorities may include such a public transport journey planner on their websites.
A trip planner may find one or more suggested paths between an origin and a destination. The origin and destination may be specified as geospatial coordinates or names of points of access to public transport such as bus stops, stations, airports or ferry ports. A location finding process may resolve the origin and destination into the nearest known nodes on the transport network in order to compute a trip plan over its data set of known paths, i.e., routes. Trip planners for large networks may use a search algorithm to search a graph of nodes (representing access points to the transport network) and edges (representing possible journeys between points). Different weightings such as distance, cost or accessibility may be associated with each edge. When suggesting the journey or trip plans, the trip planner uses the abstract model of the public transportation network and those plans may not reflect the trips that travelers make every day on these routes.
That is, the aforementioned journey planners typically ignore both the time at which a route is to be traveled and, more generally, the preferences of the passengers being served. Instead, preference is given to the shortest route or the fastest route, but not the actual routes real-world travelers took between an origin and a destination. Although these planners are increasingly reliable in their knowledge of the underlying transportation network and available services, each planner shares the same static-world assumptions. In particular, each planner makes a general assumption of constancy and universality, i.e., that the optimal trip is independent of the time and day of the actual journey and of the detailed preferences of passengers.
It will be appreciated, however, that constancy and universality are poor assumptions. Most urban travelers can verify that the best trip between work and home at midnight is not necessarily the best choice to make between the same locations at a different time, e.g., 8:00 AM. Similarly, different passengers may choose different ways to travel between the same origin and destination points.
While differences in knowledge may play a role in these divergent choices, in many cases passengers simply have different preferences about the trips they like to take. For example, one passenger may avoid multiple changes (transfers from bus to bus, train/tram to train/tram, etc.), by extending the duration of her journey by a few minutes, while another passenger simply wants to arrive as quickly as possible to the destination.
When a user queries a planner with a query to journey from an origin o to destination d starting at time s, there are often a large number of trips satisfying the query. The planner provides the k-top recommendations according to a set of predefined criteria, such as the minimal transfer time, the minimal number of changes, etc. The operations of the journey planner are similar to the manner in which information retrieval systems operate, i.e., where the goal is to place the most relevant documents among the k-top answers. It will be appreciated, therefore, that it is critical in intelligent journey planning to suggest k-top trips which reflect the real passenger preferences.
In practice, there is a divergence between the planner provided recommendations and the actual routes and/or trips taken by travelers of the transportation network. As indicated above, trips proposed by the trip planner do not necessarily correspond to choices made by real-world travelers who use the transportation network on a regular basis.
Thus, it would be advantageous to provide an effective system and method for collecting traveler information regarding real world use of a transportation network to facilitate recommendations by a trip planner to users thereof.